The present invention relates to boundary delineation of tubular structures in medical images, and more particularly, to boundary delineation of tubular structures in medical images using infinitely recurrent neural networks.
Accurate boundary delineation of tubular anatomical structures, such as airways and vessels, is important in order to obtain quantitative measurements for a wide variety of clinical scenarios. For example, in one such scenario, boundary delineation of airways in computed tomography (CT) images can be used for analyzing Chronic Obstructive Pulmonary Disorder (COPD). COPD is a disease that is both common (number three killer in the United States) and chronic, due to few curative treatments. As such, methods to diagnose COPD and monitor the effectiveness of treatments are highly relevant for physicians. An important sub-type of COPD is airway-predominant disease, where the airway walls and/or lumens are thickened due to chronic inflammation. In another scenario, boundary delineation of coronary arteries in medical image data, such as CT, is important for computational fluid dynamics (CFD) in coronary angiography cases.
The large amount of data in a CT scan presents the possibility of precise quantification of disease severity and changes, but requires automation to make this feasible. Automated quantification of airway diseases is especially problematic as the boundaries of airways in CT images may be “fuzzy” and hard to detect by conventional algorithms. In addition, nearby structures can lead to errors in defining the boundaries. When CFD is applied to vascular cases, precise delineation of vascular boundaries is critical for accurate CFD computations. In both of the above described clinical scenarios, a more accurate method for computer-based automated boundary delineation is desirable.